PO.CL09.03 · 临床研究

稳态负荷将肿瘤基因组学、疾病轨迹与诊断前可穿戴设备活动相联系

Allostatic load connects tumor genomics, disease trajectory, and pre-diagnosis wearable activity

编号 5430 展板 20 时间 4/21 09:00–12:00 区域 Section 49 主讲 Christopher Fong, BS;MS;PhD
分会场 Retrospective Observational Studies
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作者与单位 Authors & Affiliations

Christopher J. Fong1, Kaicheng U1, Cheryl Phua1, Xuechun Bai1, Karl Pichotta1, Kathryn Tsai2, Anzhi Chen1, Meixuan Zhang1, Jie Yu1, Whitney Underwood1, Chenlian Fu1, Michele Waters1, Sanna Goyert3, Adam Schoenfeld1, Nikolaus Schultz1, Justin Jee1, Jessica Scott1, Luke Pike1, Jian Carrot-Zhang1

1Memorial Sloan Kettering Cancer Center, New York, NY,2University of Illinois, Champaign, IL,3CUNY School of Medicine City College of New York, New York, NY

摘要 Abstract

中文摘要
背景:稳态负荷(AL)是来自常规化验和生命体征的生理应激综合指数,反映多系统压力并可预测癌症生存。在此前对癌症调节稳态负荷(cmAL)验证的基础上,我们研究了cmAL如何在不同疾病状态间变化、如何与肿瘤基因组学相关,以及如何与诊断前可穿戴设备测量的活动相吻合。 方法:我们分析了在MSK接受治疗的12,689例成人患者,涵盖NSCLC、结直肠癌、前列腺癌、卵巢癌、乳腺癌、胰腺癌、子宫内膜癌和膀胱癌,具有治疗前化验和生命体征数据。cmAL由十项心血管、代谢、肾脏和免疫生物标志物计算得出,并采用秩和检验加多重检验校正在不同疾病阶段间进行比较。各癌症类型的基因组模型采用多变量logistic回归,将cmAL与复发性致癌改变相关联,同时校正临床协变量和合并症负担。在77例MSK患者(Apple HealthKit)和1,867例All of Us参与者(Fitbit)中评估可穿戴设备活动,采用各cmAL水平下的中位每日步数、Spearman相关及连续趋势检验。诊断前步数采用首次癌症诊断前90天窗口进行汇总。 结果:cmAL在缓解期下降(中位变化-0.42,p<0.001),随进展而升高(中位变化+0.61,p<0.001),最高值见于死亡前六个月内(p<1×10⁻⁴⁰)。较高的cmAL与心血管代谢合并症相关(糖尿病OR=0.74;肾脏OR=0.54)。在NSCLC中,较高的cmAL在EGFR突变患者中较少见,而在KRAS突变患者中更常见。在两个可穿戴设备数据集中,较高的cmAL均与较低的诊断前活动相关。在MSK队列中,中位每日步数从cmAL=1时的4,914步降至cmAL=5时的1,092步(Spearman ρ=-0.388,p=0.0005)。在All of Us中,中位步数从cmAL=1时的6,276步降至cmAL=5时的4,091步(ρ=-0.248,p<0.0001),证明了在人群队列中的可重复性。 结论:cmAL随疾病进展而升高,并量化跨身体系统的累积生理压力。升高的cmAL在NSCLC中与特定基因组模式、更大的合并症负担以及机构和人群队列中较低的诊断前步数相关。这些发现支持cmAL作为一种可扩展且潜在可调节的生物标志物,整合真实世界癌症生存中的分子、生理和行为维度。
查看英文原文 English abstract
Background: Allostatic load (AL), a composite index of physiological stress from routine labs and vitals, reflects multisystem strain and predicts survival in cancer. Building on prior validation of cancer-modulated AL (cmAL), we examined how cmAL varies across disease states, relates to tumor genomics, and aligns with wearable-measured activity before diagnosis. Methods: We analyzed 12,689 adults treated at MSK with pre-treatment labs and vitals across NSCLC, colorectal, prostate, ovarian, breast, pancreatic, endometrial, and bladder cancers. cmAL was computed from ten cardiovascular, metabolic, renal, and immune biomarkers and compared across disease phases using rank-sum tests with multiple-testing correction. Per-cancer-type genomic models used multivariable logistic regression relating cmAL to recurrent oncogenic alterations while adjusting for clinical covariates and comorbidity burden. Wearable activity was evaluated in 77 MSK patients (Apple HealthKit) and 1,867 All of Us participants (Fitbit) using median daily step counts across cmAL levels, Spearman correlation, and continuous trend tests. Pre-diagnosis steps were summarized using a 90-day window before first cancer diagnosis. Results: cmAL decreased during remission (median change -0.42, p<0.001) and increased with progression (median change +0.61, p<0.001), with the highest values observed within six months of death (p<1×10⁻⁴⁰). Higher cmAL correlated with cardiometabolic comorbidity (diabetes OR=0.74; renal OR=0.54). In NSCLC, higher cmAL was less common in patients with EGFR mutations and more common in those with KRAS mutations. Across both wearable datasets, higher cmAL was linked to lower pre-diagnosis activity. In the MSK cohort, median daily steps declined from 4,914 at (cmAL=1) to 1,092 at (cmAL=5) (Spearman ρ=-0.388, p=0.0005). In All of Us, median steps declined from 6,276 (cmAL=1) to 4,091 (cmAL=5) (ρ=-0.248, p<0.0001), demonstrating reproducibility in a population cohort. Conclusions: cmAL increases with disease progression and quantifies cumulative physiologic strain across body systems. Elevated cmAL is associated with specific genomic patterns in NSCLC, greater comorbidity burden, and lower pre-diagnosis step counts in both institutional and population cohorts. These findings support cmAL as a scalable and potentially modifiable biomarker integrating molecular, physiologic, and behavioral domains in real-world cancer survivorship.
利益披露 Disclosure
C. J. Fong, None.. K. U, None.. C. Phua, None.. X. Bai, None.. K. Pichotta, None.. K. Tsai, None.. A. Chen, None.. M. Zhang, None.. J. Yu, None.. W. Underwood, None.. C. Fu, None.. M. Waters, None.. S. Goyert, None.. A. Schoenfeld, None.. N. Schultz, None.. J. Jee, None.. J. Scott, None.. L. Pike, None.. J. Carrot-Zhang, None.

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